Create train.py

train.py

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+from sentence_transformers import SentenceTransformer, InputExample, losses, evaluation
+from torch.utils.data import DataLoader
+import json
+import numpy as np
+
+# 1. Load data
+with open('data/listings.json') as f:
+    train_data = json.load(f)
+
+# 2. Prepare examples
+train_examples = []
+for item in train_data:
+    train_examples.append(InputExample(
+        texts=[item['text']],
+        label=item['category_id']
+    ))
+
+# 3. Initialize model
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# 4. Train with contrastive loss
+train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=16)
+loss = losses.ContrastiveLoss(model=model)
+
+model.fit(
+    train_objectives=[(train_dataloader, loss)],
+    epochs=3,
+    warmup_steps=100
+)
+
+# 5. Save model
+model.save('models/ad_categorizer')
+print("Training complete! Model saved.")